Messaging system with avatar generation

ABSTRACT

A system comprises one or more processors of a machine and a memory storing instructions that, when executed by the one or more processors, cause the machine to perform operations. The operations comprise: receiving an image; generating an avatar with a trained neural network based on the image, the trained neural network predicting multiple trait values for the avatar; and sending a message with the generated avatar.

TECHNICAL FIELD

The present disclosure relates to messaging systems, and particularly,but not exclusively, to training a neural network to generate useravatars based on user images.

BACKGROUND

Avatars are graphical representations of users used in computingsystems, such as video games, messaging systems, Internet forums, etc.Avatars can be used to graphically identify a message sender. Typically,users can select an avatar from a preset list or upload an avatar image.Alternatively, users can design their own avatars.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

To easily identify the discussion of any particular element or act, themost significant digit or digits in a reference number refer to thefigure number in which that element is first introduced.

FIG. 1 is a diagrammatic representation of a networked environment inwhich the present disclosure may be deployed, in accordance with someexample embodiments.

FIG. 2 is a diagrammatic representation of a messaging clientapplication, in accordance with some example embodiments.

FIG. 3 is a diagrammatic representation of a data structure asmaintained in a database, in accordance with some example embodiments.

FIG. 4 is a diagrammatic representation of a message, in accordance withsome example embodiments.

FIG. 5 is a flowchart for an access-limiting process, in accordance withsome example embodiments.

FIG. 6 is block diagram showing a software architecture within which thepresent disclosure may be implemented, in accordance with some exampleembodiments.

FIG. 7 is a diagrammatic representation of a machine, in the form of acomputer system within which a set of instructions may be executed forcausing the machine to perform any one or more of the methodologiesdiscussed, in accordance with some example embodiments.

FIG. 8 is a diagrammatic representation of a processing environment, inaccordance with some example embodiments.

FIG. 9 illustrates a neural network in accordance with one embodiment.

FIG. 10 illustrates a method of pretraining in accordance with oneembodiment.

FIG. 11 illustrates a method of neural network training in accordancewith one embodiment.

FIG. 12 illustrates a method of generating an avatar in accordance withone embodiment.

FIG. 13 illustrates a learning rate parameter curve.

DETAILED DESCRIPTION

Given a self-image (“selfie”), a trained neural network can generate anavatar. Generating an avatar comprises selecting a specific value foreach facial trait, for example “black” hair tone, “short-straight” hairstyle, “wide” nose style, etc. There are over 20 traits that can bechanged in addition to selecting two genders: male or female. Further,there are multiple avatar styles. Each style will have a different setof traits that can be changed and different looking values for eachtrait.

FIG. 1 is a block diagram showing an example messaging system 100 forexchanging data (e.g., messages and associated content) over a network.The messaging system 100 includes multiple instances of a client device102, each of which hosts a number of applications including a messagingclient application 104. Each messaging client application 104 iscommunicatively coupled to other instances of the messaging clientapplication 104 and a messaging server system 108 via a network 106(e.g., the Internet).

A messaging client application 104 is able to communicate and exchangedata with another messaging client application 104 and with themessaging server system 108 via the network 106. The data exchangedbetween messaging client application 104, and between a messaging clientapplication 104 and the messaging server system 108, includes functions(e.g., commands to invoke functions) as well as payload data (e.g.,text, audio, video or other multimedia data).

The messaging server system 108 provides server-side functionality viathe network 106 to a particular messaging client application 104. Whilecertain functions of the messaging system 100 are described herein asbeing performed by either a messaging client application 104 or by themessaging server system 108, the location of certain functionalityeither within the messaging client application 104 or the messagingserver system 108 is a design choice. For example, it may be technicallypreferable to initially deploy certain technology and functionalitywithin the messaging server system 108, but to later migrate thistechnology and functionality to the messaging client application 104where a client device 102 has a sufficient processing capacity.

The messaging server system 108 supports various services and operationsthat are provided to the messaging client application 104. Suchoperations include transmitting data to, receiving data from, andprocessing data generated by the messaging client application 104. Thisdata may include, message content, client device information,geolocation information, media annotation and overlays, message contentpersistence conditions, social network information, and live eventinformation, as examples. Data exchanges within the messaging system 100are invoked and controlled through functions available via userinterfaces (UIs) of the messaging client application 104.

Turning now specifically to the messaging server system 108, anApplication Program Interface (API) server 110 is coupled to, andprovides a programmatic interface to, an application server 112. Theapplication server 112 is communicatively coupled to a database server118, which facilitates access to a database 120 in which is stored dataassociated with messages processed by the application server 112.

The Application Program Interface (API) server 110 receives andtransmits message data (e.g., commands and message payloads) between theclient device 102 and the application server 112. Specifically, theApplication Program Interface (API) server 110 provides a set ofinterfaces (e.g., routines and protocols) that can be called or queriedby the messaging client application 104 in order to invoke functionalityof the application server 112. The Application Program Interface (API)server 110 exposes various functions supported by the application server112, including account registration, login functionality, the sending ofmessages, via the application server 112, from a particular messagingclient application 104 to another messaging client application 104, thesending of media files (e.g., images or video) from a messaging clientapplication 104 to the messaging server application 114, and forpossible access by another messaging client application 104, the settingof a collection of media data (e.g., story), the retrieval of a list offriends of a user of a client device 102, the retrieval of suchcollections, the retrieval of messages and content, the adding anddeletion of friends to a social graph, the location of friends within asocial graph, and opening an application event (e.g., relating to themessaging client application 104).

The application server 112 hosts a number of applications andsubsystems, including a messaging server application 114, an imageprocessing system 116 and a social network system 122. The messagingserver application 114 implements a number of message processingtechnologies and functions, particularly related to the aggregation andother processing of content (e.g., textual and multimedia content)included in messages received from multiple instances of the messagingclient application 104. As will be described in further detail, the textand media content from multiple sources may be aggregated intocollections of content (e.g., called stories or galleries). Thesecollections are then made available, by the messaging server application114, to the messaging client application 104. Other processor and memoryintensive processing of data may also be performed server-side by themessaging server application 114, in view of the hardware requirementsfor such processing.

The application server 112 also includes an image processing system 116that is dedicated to performing various image processing operations,typically with respect to images or video received within the payload ofa message at the messaging server application 114.

The social network system 122 supports various social networkingfunctions services, and makes these functions and services available tothe messaging server application 114. To this end, the social networksystem 122 maintains and accesses an entity graph 304 (as shown in FIG.3) within the database 120. Examples of functions and services supportedby the social network system 122 include the identification of otherusers of the messaging system 100 with which a particular user hasrelationships or is “following”, and also the identification of otherentities and interests of a particular user.

The application server 112 is communicatively coupled to a databaseserver 118, which facilitates access to a database 120 in which isstored data associated with messages processed by the messaging serverapplication 114.

FIG. 2 is block diagram illustrating further details regarding themessaging system 100, according to example embodiments. Specifically,the messaging system 100 is shown to comprise the messaging clientapplication 104 and the application server 112, which in turn embody anumber of some subsystems, namely an ephemeral timer system 202, acollection management system 204 and an annotation system 206.

The ephemeral timer system 202 is responsible for enforcing thetemporary access to content permitted by the messaging clientapplication 104 and the messaging server application 114. To this end,the ephemeral timer system 202 incorporates a number of timers that,based on duration and display parameters associated with a message, orcollection of messages (e.g., a story), selectively display and enableaccess to messages and associated content via the messaging clientapplication 104. Further details regarding the operation of theephemeral timer system 202 are provided below.

The collection management system 204 is responsible for managingcollections of media (e.g., collections of text, image video and audiodata). In some examples, a collection of content (e.g., messages,including images, video, text and audio) may be organized into an “eventgallery” or an “event story.” Such a collection may be made availablefor a specified time period, such as the duration of an event to whichthe content relates. For example, content relating to a music concertmay be made available as a “story” for the duration of that musicconcert. The collection management system 204 may also be responsiblefor publishing an icon that provides notification of the existence of aparticular collection to the user interface of the messaging clientapplication 104.

The collection management system 204 furthermore includes a curationinterface 208 that allows a collection manager to manage and curate aparticular collection of content. For example, the curation interface208 enables an event organizer to curate a collection of contentrelating to a specific event (e.g., delete inappropriate content orredundant messages). Additionally, the collection management system 204employs machine vision (or image recognition technology) and contentrules to automatically curate a content collection. In certainembodiments, compensation may be paid to a user for inclusion ofuser-generated content into a collection. In such cases, the curationinterface 208 operates to automatically make payments to such users forthe use of their content.

The annotation system 206 provides various functions that enable a userto annotate or otherwise modify or edit media content associated with amessage. For example, the annotation system 206 provides functionsrelated to the generation and publishing of media overlays for messagesprocessed by the messaging system 100. The annotation system 206operatively supplies a media overlay or supplementation (e.g., an imagefilter) to the messaging client application 104 based on a geolocationof the client device 102. In another example, the annotation system 206operatively supplies a media overlay to the messaging client application104 based on other information, such as social network information ofthe user of the client device 102. A media overlay may include audio andvisual content and visual effects. Examples of audio and visual contentinclude pictures, texts, logos, animations, and sound effects. Anexample of a visual effect includes color overlaying. The audio andvisual content or the visual effects can be applied to a media contentitem (e.g., a photo) at the client device 102. For example, the mediaoverlay may include text that can be overlaid on top of a photographtaken by the client device 102. In another example, the media overlayincludes an identification of a location overlay (e.g., Venice beach), aname of a live event, or a name of a merchant overlay (e.g., BeachCoffee House). In another example, the annotation system 206 uses thegeolocation of the client device 102 to identify a media overlay thatincludes the name of a merchant at the geolocation of the client device102. The media overlay may include other indicia associated with themerchant. The media overlays may be stored in the database 120 andaccessed through the database server 118.

In one example embodiment, the annotation system 206 provides auser-based publication platform that enables users to select ageolocation on a map, and upload content associated with the selectedgeolocation. The user may also specify circumstances under which aparticular media overlay should be offered to other users. Theannotation system 206 generates a media overlay that includes theuploaded content and associates the uploaded content with the selectedgeolocation.

In another example embodiment, the annotation system 206 provides amerchant-based publication platform that enables merchants to select aparticular media overlay associated with a geolocation via a biddingprocess. For example, the annotation system 206 associates the mediaoverlay of a highest bidding merchant with a corresponding geolocationfor a predefined amount of time.

FIG. 3 is a schematic diagram illustrating data structures 300 which maybe stored in the database 120 of the messaging server system 108,according to certain example embodiments. While the content of thedatabase 120 is shown to comprise a number of tables, it will beappreciated that the data could be stored in other types of datastructures (e.g., as an object-oriented database).

The database 120 includes message data stored within a message table314. The entity table 302 stores entity data, including an entity graph304. Entities for which records are maintained within the entity table302 may include individuals, corporate entities, organizations, objects,places, events, etc. Regardless of type, any entity regarding which themessaging server system 108 stores data may be a recognized entity. Eachentity is provided with a unique identifier, as well as an entity typeidentifier (not shown).

The entity graph 304 furthermore stores information regardingrelationships and associations between entities. Such relationships maybe social, professional (e.g., work at a common corporation ororganization) interested-based or activity-based, merely for example.

The database 120 also stores annotation data, in the example form offilters, in an annotation table 312. Filters for which data is storedwithin the annotation table 312 are associated with and applied tovideos (for which data is stored in a video table 310) and/or images(for which data is stored in an image table 308). Filters, in oneexample, are overlays that are displayed as overlaid on an image orvideo during presentation to a recipient user. Filters may be of variestypes, including user-selected filters from a gallery of filterspresented to a sending user by the messaging client application 104 whenthe sending user is composing a message. Other types of filters includegeolocation filters (also known as geo-filters) which may be presentedto a sending user based on geographic location. For example, geolocationfilters specific to a neighborhood or special location may be presentedwithin a user interface by the messaging client application 104, basedon geolocation information determined by a GPS unit of the client device102. Another type of filer is a data filer, which may be selectivelypresented to a sending user by the messaging client application 104,based on other inputs or information gathered by the client device 102during the message creation process. Example of data filters includecurrent temperature at a specific location, a current speed at which asending user is traveling, battery life for a client device 102, or thecurrent time.

Other annotation data that may be stored within the image table 308 isso-called “lens” data. A “lens” may be a real-time special effect andsound that may be added to an image or a video.

As mentioned above, the video table 310 stores video data which, in oneembodiment, is associated with messages for which records are maintainedwithin the message table 314. Similarly, the image table 308 storesimage data associated with messages for which message data is stored inthe entity table 302. The entity table 302 may associate variousannotations from the annotation table 312 with various images and videosstored in the image table 308 and the video table 310.

A story table 306 stores data regarding collections of messages andassociated image, video, or audio data, which are compiled into acollection (e.g., a story or a gallery). The creation of a particularcollection may be initiated by a particular user (e.g., each user forwhich a record is maintained in the entity table 302). A user may createa “personal story” in the form of a collection of content that has beencreated and sent/broadcast by that user. To this end, the user interfaceof the messaging client application 104 may include an icon that isuser-selectable to enable a sending user to add specific content to hisor her personal story.

A collection may also constitute a “live story,” which is a collectionof content from multiple users that is created manually, automatically,or using a combination of manual and automatic techniques. For example,a “live story” may constitute a curated stream of user-submitted contentfrom varies locations and events. Users whose client devices havelocation services enabled and are at a common location event at aparticular time may, for example, be presented with an option, via auser interface of the messaging client application 104, to contributecontent to a particular live story. The live story may be identified tothe user by the messaging client application 104, based on his or herlocation. The end result is a “live story” told from a communityperspective.

A further type of content collection is known as a “location story”,which enables a user whose client device 102 is located within aspecific geographic location (e.g., on a college or university campus)to contribute to a particular collection. In some embodiments, acontribution to a location story may require a second degree ofauthentication to verify that the end user belongs to a specificorganization or other entity (e.g., is a student on the universitycampus).

FIG. 4 is a schematic diagram illustrating a structure of a message 400,according to some in some embodiments, generated by a messaging clientapplication 104 for communication to a further messaging clientapplication 104 or the messaging server application 114. The content ofa particular message 400 is used to populate the message table 314stored within the database 120, accessible by the messaging serverapplication 114. Similarly, the content of a message 400 is stored inmemory as “in-transit” or “in-flight” data of the client device 102 orthe application server 112. The message 400 is shown to include thefollowing components:

-   -   A message identifier 402: a unique identifier that identifies        the message 400.    -   A message text payload 404: text, to be generated by a user via        a user interface of the client device 102 and that is included        in the message 400.    -   A message image payload 406: image data, captured by a camera        component of a client device 102 or retrieved from a memory        component of a client device 102, and that is included in the        message 400.    -   A message video payload 408: video data, captured by a camera        component or retrieved from a memory component of the client        device 102 and that is included in the message 400.    -   A message audio payload 410: audio data, captured by a        microphone or retrieved from a memory component of the client        device 102, and that is included in the message 400.    -   A message annotations 412: annotation data (e.g., filters,        stickers or other enhancements) that represents annotations to        be applied to message image payload 406, message video payload        408, or message audio payload 410 of the message 400.    -   A message duration parameter 414: parameter value indicating, in        seconds, the amount of time for which content of the message        (e.g., the message image payload 406, message video payload 408,        message audio payload 410) is to be presented or made accessible        to a user via the messaging client application 104.    -   A message geolocation parameter 416: geolocation data (e.g.,        latitudinal and longitudinal coordinates) associated with the        content payload of the message. Multiple message geolocation        parameter 416 values may be included in the payload, each of        these parameter values being associated with respect to content        items included in the content (e.g., a specific image into        within the message image payload 406, or a specific video in the        message video payload 408).    -   A message story identifier 418: identifier values identifying        one or more content collections (e.g., “stories”) with which a        particular content item in the message image payload 406 of the        message 400 is associated. For example, multiple images within        the message image payload 406 may each be associated with        multiple content collections using identifier values.    -   A message tag 420: each message 400 may be tagged with multiple        tags, each of which is indicative of the subject matter of        content included in the message payload. For example, where a        particular image included in the message image payload 406        depicts an animal (e.g., a lion), a tag value may be included        within the message tag 420 that is indicative of the relevant        animal. Tag values may be generated manually, based on user        input, or may be automatically generated using, for example,        image recognition.    -   A message sender identifier 422: an identifier (e.g., a        messaging system identifier, email address, or device        identifier) indicative of a user of the client device 102 on        which the message 400 was generated and from which the message        400 was sent    -   A message receiver identifier 424: an identifier (e.g., a        messaging system identifier, email address, or device        identifier) indicative of a user of the client device 102 to        which the message 400 is addressed.

The contents (e.g., values) of the various components of message 400 maybe pointers to locations in tables within which content data values arestored. For example, an image value in the message image payload 406 maybe a pointer to (or address of) a location within an image table 308.Similarly, values within the message video payload 408 may point to datastored within a video table 310, values stored within the messageannotations 412 may point to data stored in an annotation table 312,values stored within the message story identifier 418 may point to datastored in a story table 306, and values stored within the message senderidentifier 422 and the message receiver identifier 424 may point to userrecords stored within an entity table 302.

FIG. 5 is a schematic diagram illustrating an access-limiting process500, in terms of which access to content (e.g., an ephemeral message502, and associated multimedia payload of data) or a content collection(e.g., an ephemeral message group 504) may be time-limited (e.g., madeephemeral).

An ephemeral message 502 is shown to be associated with a messageduration parameter 506, the value of which determines an amount of timethat the ephemeral message 502 will be displayed to a receiving user ofthe ephemeral message 502 by the messaging client application 104. Inone embodiment, an ephemeral message 502 is viewable by a receiving userfor up to a maximum of 10 seconds, depending on the amount of time thatthe sending user specifies using the message duration parameter 506.

The message duration parameter 506 and the message receiver identifier424 are shown to be inputs to a message timer 512, which is responsiblefor determining the amount of time that the ephemeral message 502 isshown to a particular receiving user identified by the message receiveridentifier 424. In particular, the ephemeral message 502 will only beshown to the relevant receiving user for a time period determined by thevalue of the message duration parameter 506. The message timer 512 isshown to provide output to a more generalized ephemeral timer system202, which is responsible for the overall timing of display of content(e.g., an ephemeral message 502) to a receiving user.

The ephemeral message 502 is shown in FIG. 5 to be included within anephemeral message group 504 (e.g., a collection of messages in apersonal story, or an event story). The ephemeral message group 504 hasan associated group duration parameter 508, a value of which determinesa time-duration for which the ephemeral message group 504 is presentedand accessible to users of the messaging system 100. The group durationparameter 508, for example, may be the duration of a music concert,where the ephemeral message group 504 is a collection of contentpertaining to that concert. Alternatively, a user (either the owninguser or a curator user) may specify the value for the group durationparameter 508 when performing the setup and creation of the ephemeralmessage group 504.

Additionally, each ephemeral message 502 within the ephemeral messagegroup 504 has an associated group participation parameter 510, a valueof which determines the duration of time for which the ephemeral message502 will be accessible within the context of the ephemeral message group504. Accordingly, a particular ephemeral message group 504 may “expire”and become inaccessible within the context of the ephemeral messagegroup 504, prior to the ephemeral message group 504 itself expiring interms of the group duration parameter 508. The group duration parameter508, group participation parameter 510, and message receiver identifier424 each provide input to a group timer 514, which operationallydetermines, firstly, whether a particular ephemeral message 502 of theephemeral message group 504 will be displayed to a particular receivinguser and, if so, for how long. Note that the ephemeral message group 504is also aware of the identity of the particular receiving user as aresult of the message receiver identifier 424.

Accordingly, the group timer 514 operationally controls the overalllifespan of an associated ephemeral message group 504, as well as anindividual ephemeral message 502 included in the ephemeral message group504. In one embodiment, each and every ephemeral message 502 within theephemeral message group 504 remains viewable and accessible for atime-period specified by the group duration parameter 508. In a furtherembodiment, a certain ephemeral message 502 may expire, within thecontext of ephemeral message group 504, based on a group participationparameter 510. Note that a message duration parameter 506 may stilldetermine the duration of time for which a particular ephemeral message502 is displayed to a receiving user, even within the context of theephemeral message group 504. Accordingly, the message duration parameter506 determines the duration of time that a particular ephemeral message502 is displayed to a receiving user, regardless of whether thereceiving user is viewing that ephemeral message 502 inside or outsidethe context of an ephemeral message group 504.

The ephemeral timer system 202 may furthermore operationally remove aparticular ephemeral message 502 from the ephemeral message group 504based on a determination that it has exceeded an associated groupparticipation parameter 510. For example, when a sending user hasestablished a group participation parameter 510 of 24 hours fromposting, the ephemeral timer system 202 will remove the relevantephemeral message 502 from the ephemeral message group 504 after thespecified 24 hours. The ephemeral timer system 202 also operates toremove an ephemeral message group 504 either when the groupparticipation parameter 510 for each and every ephemeral message 502within the ephemeral message group 504 has expired, or when theephemeral message group 504 itself has expired in terms of the groupduration parameter 508.

In certain use cases, a creator of a particular ephemeral message group504 may specify an indefinite group duration parameter 508. In thiscase, the expiration of the group participation parameter 510 for thelast remaining ephemeral message 502 within the ephemeral message group504 will determine when the ephemeral message group 504 itself expires.In this case, a new ephemeral message 502, added to the ephemeralmessage group 504, with a new group participation parameter 510,effectively extends the life of an ephemeral message group 504 to equalthe value of the group participation parameter 510.

Responsive to the ephemeral timer system 202 determining that anephemeral message group 504 has expired (e.g., is no longer accessible),the ephemeral timer system 202 communicates with the messaging system100 (and, for example, specifically the messaging client application104) to cause an indicium (e.g., an icon) associated with the relevantephemeral message group 504 to no longer be displayed within a userinterface of the messaging client application 104. Similarly, when theephemeral timer system 202 determines that the message durationparameter 506 for a particular ephemeral message 502 has expired, theephemeral timer system 202 causes the messaging client application 104to no longer display an indicium (e.g., an icon or textualidentification) associated with the ephemeral message 502.

FIG. 6 is a block diagram 600 illustrating a software architecture 604,which can be installed on any one or more of the devices describedherein. The software architecture 604 is supported by hardware such as amachine 602 that includes processors 620, memory 626, and I/O components638. In this example, the software architecture 604 can beconceptualized as a stack of layers, where each layer provides aparticular functionality. The software architecture 604 includes layerssuch as an operating system 612, libraries 610, frameworks 608, andapplications 606. Operationally, the applications 606 invoke API calls650 through the software stack and receive messages 652 in response tothe API calls 650.

The operating system 612 manages hardware resources and provides commonservices. The operating system 612 includes, for example, a kernel 614,services 616, and drivers 622. The kernel 614 acts as an abstractionlayer between the hardware and the other software layers. For example,the kernel 614 provides memory management, processor management (e.g.,scheduling), component management, networking, and security settings,among other functionality. The services 616 can provide other commonservices for the other software layers. The drivers 622 are responsiblefor controlling or interfacing with the underlying hardware. Forinstance, the drivers 622 can include display drivers, camera drivers,BLUETOOTH® or BLUETOOTH® Low Energy drivers, flash memory drivers,serial communication drivers (e.g., Universal Serial Bus (USB) drivers),WI-FI® drivers, audio drivers, power management drivers, and so forth.

The libraries 610 provide a low-level common infrastructure used by theapplications 606. The libraries 610 can include system libraries 618(e.g., C standard library) that provide functions such as memoryallocation functions, string manipulation functions, mathematicfunctions, and the like. In addition, the libraries 610 can include APIlibraries 624 such as media libraries (e.g., libraries to supportpresentation and manipulation of various media formats such as MovingPicture Experts Group-4 (MPEG4), Advanced Video Coding (H.264 or AVC),Moving Picture Experts Group Layer-3 (MP3), Advanced Audio Coding (AAC),Adaptive Multi-Rate (AMR) audio codec, Joint Photographic Experts Group(JPEG or JPG), or Portable Network Graphics (PNG)), graphics libraries(e.g., an OpenGL framework used to render in two dimensions (2D) andthree dimensions (3D) in a graphic content on a display), databaselibraries (e.g., SQLite to provide various relational databasefunctions), web libraries (e.g., WebKit to provide web browsingfunctionality), and the like. The libraries 610 can also include a widevariety of other libraries 628 to provide many other APIs to theapplications 606.

The frameworks 608 provide a high-level common infrastructure that isused by the applications 606. For example, the frameworks 608 providevarious graphical user interface (GUI) functions, high-level resourcemanagement, and high-level location services. The frameworks 608 canprovide a broad spectrum of other APIs that can be used by theapplications 606, some of which may be specific to a particularoperating system or platform.

In an example embodiment, the applications 606 may include a homeapplication 636, a contacts application 630, a browser application 632,a book reader application 634, a location application 642, a mediaapplication 644, a messaging application 646, a game application 648,and a broad assortment of other applications such as third-partyapplications 640. The applications 606 are programs that executefunctions defined in the programs. Various programming languages can beemployed to create one or more of the applications 606, structured in avariety of manners, such as object-oriented programming languages (e.g.,Objective-C, Java, or C++) or procedural programming languages (e.g., Cor assembly language). In a specific example, the third-partyapplications 640 (e.g., applications developed using the ANDROID™ orIOS™ software development kit (SDK) by an entity other than the vendorof the particular platform) may be mobile software running on a mobileoperating system such as IOS™, ANDROID™, WINDOWS® Phone, or anothermobile operating system. In this example, the third-party applications640 can invoke the API calls 650 provided by the operating system 612 tofacilitate functionality described herein.

FIG. 7 is a diagrammatic representation of a machine 700 within whichinstructions 708 (e.g., software, a program, an application, an applet,an app, or other executable code) for causing the machine 700 to performany one or more of the methodologies discussed herein may be executed.For example, the instructions 708 may cause the machine 700 to executeany one or more of the methods described herein. The instructions 708transform the general, non-programmed machine 700 into a particularmachine 700 programmed to carry out the described and illustratedfunctions in the manner described. The machine 700 may operate as astandalone device or may be coupled (e.g., networked) to other machines.In a networked deployment, the machine 700 may operate in the capacityof a server machine or a client machine in a server-client networkenvironment, or as a peer machine in a peer-to-peer (or distributed)network environment. The machine 700 may comprise, but not be limitedto, a server computer, a client computer, a personal computer (PC), atablet computer, a laptop computer, a netbook, a set-top box (STB), aPDA, an entertainment media system, a cellular telephone, a smart phone,a mobile device, a wearable device (e.g., a smart watch), a smart homedevice (e.g., a smart appliance), other smart devices, a web appliance,a network router, a network switch, a network bridge, or any machinecapable of executing the instructions 708, sequentially or otherwise,that specify actions to be taken by the machine 700. Further, while onlya single machine 700 is illustrated, the term “machine” shall also betaken to include a collection of machines that individually or jointlyexecute the instructions 708 to perform any one or more of themethodologies discussed herein.

The machine 700 may include processors 702, memory 704, and I/Ocomponents 742, which may be configured to communicate with each othervia a bus 744. In an example embodiment, the processors 702 (e.g., aCentral Processing Unit (CPU), a Reduced Instruction Set Computing(RISC) processor, a Complex Instruction Set Computing (CISC) processor,a Graphics Processing Unit (GPU), a Digital Signal Processor (DSP), anASIC, a Radio-Frequency Integrated Circuit (RFIC), another processor, orany suitable combination thereof) may include, for example, a processor706 and a processor 710 that execute the instructions 708. The term“processor” is intended to include multi-core processors that maycomprise two or more independent processors (sometimes referred to as“cores”) that may execute instructions contemporaneously. Although FIG.7 shows multiple processors 702, the machine 700 may include a singleprocessor with a single core, a single processor with multiple cores(e.g., a multi-core processor), multiple processors with a single core,multiple processors with multiples cores, or any combination thereof.

The memory 704 includes a main memory 712, a static memory 714, and astorage unit 716, both accessible to the processors 702 via the bus 744.The main memory 704, the static memory 714, and storage unit 716 storethe instructions 708 embodying any one or more of the methodologies orfunctions described herein. The instructions 708 may also reside,completely or partially, within the main memory 712, within the staticmemory 714, within machine-readable medium 718 within the storage unit716, within at least one of the processors 702 (e.g., within theprocessor's cache memory), or any suitable combination thereof, duringexecution thereof by the machine 700.

The I/O components 742 may include a wide variety of components toreceive input, provide output, produce output, transmit information,exchange information, capture measurements, and so on. The specific I/Ocomponents 742 that are included in a particular machine will depend onthe type of machine. For example, portable machines such as mobilephones may include a touch input device or other such input mechanisms,while a headless server machine will likely not include such a touchinput device. It will be appreciated that the I/O components 742 mayinclude many other components that are not shown in FIG. 7. In variousexample embodiments, the I/O components 742 may include outputcomponents 728 and input components 730. The output components 728 mayinclude visual components (e.g., a display such as a plasma displaypanel (PDP), a light emitting diode (LED) display, a liquid crystaldisplay (LCD), a projector, or a cathode ray tube (CRT)), acousticcomponents (e.g., speakers), haptic components (e.g., a vibratory motor,resistance mechanisms), other signal generators, and so forth. The inputcomponents 730 may include alphanumeric input components (e.g., akeyboard, a touch screen configured to receive alphanumeric input, aphoto-optical keyboard, or other alphanumeric input components),point-based input components (e.g., a mouse, a touchpad, a trackball, ajoystick, a motion sensor, or another pointing instrument), tactileinput components (e.g., a physical button, a touch screen that provideslocation and/or force of touches or touch gestures, or other tactileinput components), audio input components (e.g., a microphone), and thelike.

In further example embodiments, the I/O components 742 may includebiometric components 732, motion components 734, environmentalcomponents 736, or position components 738, among a wide array of othercomponents. For example, the biometric components 732 include componentsto detect expressions (e.g., hand expressions, facial expressions, vocalexpressions, body gestures, or eye tracking), measure biosignals (e.g.,blood pressure, heart rate, body temperature, perspiration, or brainwaves), identify a person (e.g., voice identification, retinalidentification, facial identification, fingerprint identification, orelectroencephalogram-based identification), and the like. The motioncomponents 734 include acceleration sensor components (e.g.,accelerometer), gravitation sensor components, rotation sensorcomponents (e.g., gyroscope), and so forth. The environmental components736 include, for example, illumination sensor components (e.g.,photometer), temperature sensor components (e.g., one or morethermometers that detect ambient temperature), humidity sensorcomponents, pressure sensor components (e.g., barometer), acousticsensor components (e.g., one or more microphones that detect backgroundnoise), proximity sensor components (e.g., infrared sensors that detectnearby objects), gas sensors (e.g., gas detection sensors to detectionconcentrations of hazardous gases for safety or to measure pollutants inthe atmosphere), or other components that may provide indications,measurements, or signals corresponding to a surrounding physicalenvironment. The position components 738 include location sensorcomponents (e.g., a GPS receiver component), altitude sensor components(e.g., altimeters or barometers that detect air pressure from whichaltitude may be derived), orientation sensor components (e.g.,magnetometers), and the like.

Communication may be implemented using a wide variety of technologies.The I/O components 742 further include communication components 740operable to couple the machine 700 to a network 720 or devices 722 via acoupling 724 and a coupling 726, respectively. For example, thecommunication components 740 may include a network interface componentor another suitable device to interface with the network 720. In furtherexamples, the communication components 740 may include wiredcommunication components, wireless communication components, cellularcommunication components, Near Field Communication (NFC) components,Bluetooth® components (e.g., Bluetooth® Low Energy), Wi-Fi® components,and other communication components to provide communication via othermodalities. The devices 722 may be another machine or any of a widevariety of peripheral devices (e.g., a peripheral device coupled via aUSB).

Moreover, the communication components 740 may detect identifiers orinclude components operable to detect identifiers. For example, thecommunication components 740 may include Radio Frequency Identification(RFID) tag reader components, NFC smart tag detection components,optical reader components (e.g., an optical sensor to detectone-dimensional bar codes such as Universal Product Code (UPC) bar code,multi-dimensional bar codes such as Quick Response (QR) code, Azteccode, Data Matrix, Dataglyph, MaxiCode, PDF417, Ultra Code, UCC RSS-2Dbar code, and other optical codes), or acoustic detection components(e.g., microphones to identify tagged audio signals). In addition, avariety of information may be derived via the communication components740, such as location via Internet Protocol (IP) geolocation, locationvia Wi-Fi® signal triangulation, location via detecting an NFC beaconsignal that may indicate a particular location, and so forth.

The various memories (e.g., memory 704, main memory 712, static memory714, and/or memory of the processors 702) and/or storage unit 716 maystore one or more sets of instructions and data structures (e.g.,software) embodying or used by any one or more of the methodologies orfunctions described herein. These instructions (e.g., the instructions708), when executed by processors 702, cause various operations toimplement the disclosed embodiments.

The instructions 708 may be transmitted or received over the network720, using a transmission medium, via a network interface device (e.g.,a network interface component included in the communication components740) and using any one of a number of well-known transfer protocols(e.g., hypertext transfer protocol (HTTP)). Similarly, the instructions708 may be transmitted or received using a transmission medium via thecoupling 726 (e.g., a peer-to-peer coupling) to the devices 722.

Turning now to FIG. 8, there is shown a diagrammatic representation of aprocessing environment 800, which includes the processor 806, theprocessor 808, and a processor 802 (e.g., a GPU, CPU or combinationthereof).

The processor 802 is shown to be coupled to a power source 804, and toinclude (either permanently configured or temporarily instantiated)modules, namely an Imager 810, a graphical user interface GUI 812, anAvatar Generator 814, Avatar Data 816 and a Geolocator 818. The Imager810 operationally captures a self-photograph (“image”) of the user, theGUI 812 operationally generates a user interface for a user to interactwith the Avatar Generator 814, and the Avatar Generator 814operationally generates an avatar using the self-photograph and theavatar data. During a training phase, the Geolocator 818 sendsgeolocation to a neural network 900 (FIG. 9) as will be discussedfurther below. During the training phase, the user can generate anavatar with the Avatar Generator 814. The generated avatar,self-photograph and location determined by the Geolocator 818 it thensent to the neural network 900 for training. This can be repeated formultiple users (hundreds to millions or more) to ensure appropriatetraining data for the neural network 900. Training of the neural network900 will be discussed in further detail in conjunction with FIG. 9-FIG.11.

Once the neural network 900 is trained, the processing environment 800receives Avatar Data 816 from the neural network 900. The Avatar Data816 includes predicted avatar values for each facial trait and gender.The Avatar Data 816 can also include different predicted avatar valuesfor each facial trait based on location of the user as indicated by theGeolocator 818. The Avatar Data 816 may also include different predictedvalues based on avatar style. The Avatar Generator 814 then generates anavatar for a user based on the Avatar Data 816 and a photograph of theuser. The Avatar Generator 814 can also base the avatar generation basedon location and avatar style (e.g., Bitstrips, Bitmoji Classic, BitmojiDeluxe) selected by a user. Alternatively, the Avatar Generator 814transmits the photograph to the trained neural network 900 to generatethe avatar. Alternatively, the Avatar Generator 814 includes the trainedneural network 900 and performs avatar generation locally.

FIG. 9 illustrates a neural network 900 in accordance with oneembodiment. The neural network 900 trains on and predicts multiple traitvalues in a single neural network. This corresponds to having multipledimensions of classes to predict. Further the neural network 900 usesdynamic balancing of the dataset at training time independently acrossmultiple traits and performs dynamic filtering of the dataset attraining time independently across multiple traits. The neural network900 allows for decomposition of traits into subtraits and incorporates atransfer learning methodology to retrain the neural network 900 on alimited dataset when the set of traits or the set of trait valueschanges.

The neural network 900 collects a dataset of hundreds to tens ofmillions or more of self-images taken by users and the avatars that theydesigned. The neural network 900 is modeled as a classification problemacross multiple dimensions (traits). The neural network 900 sharesbottom layers (“close” to the pixels of the image) with an open sourceneural network (e.g., mobilenet) but replaces the top classificationlayers with a fanout of top classification layers (“close” to thepredicted avatar trait values). This reduces the neural network size andwhile increasing speed of execution which is a requirement for runningthe inference on smartphone devices.

However, the dataset can be very noisy. Problems include “fantasy”avatars that do not resemble the face in the self-image or “partial”avatars that were poorly designed by un-engaged users. Accordingly, thedataset if filtered by removing “partial” avatars based on the number oftraits the user changed from the default avatar. Further, the values forany given trait are unbalanced. This means that there are wildly popularand wildly unpopular hairstyles and also hairstyles in between. It wouldbe easy for the neural network 900 to learn to always predict the mostpopular hairstyle and be right most of the time.

The neural network 900 applies balancing across multiple dimensions.Duplication may not work because different traits would specify adifferent duplication schedule. Multiplying gradients by the inversefrequency caused other problems:

-   -   Unpopular trait values would be over predicted. Instead, neural        network 900 multiplies gradient by square root of inverse        frequency instead.    -   Dynamically filter out trait values that are wildly unpopular.        Accordingly, wouldn't propagate a gradient across certain paths        of the fan out while still propagating the gradient across other        paths. This minimizes reducing the training dataset        exorbitantly, otherwise filtering on a single data item        (photograph & avatar trait values) would cause overfiltering.        Some users do not put much effort into designing their avatar        which causes us to collect noisy training data which encourages        predictions close to the default avatar regardless of the        contents of the selfie image. To resolve this issue any training        data item that contains the default value for any of these        traits gets filtered out of the training dataset: skin tone,        hair style, pupil tone.    -   Loss function normalization per class has to be done carefully        because effective learning rate should stay relatively constant.        Naive methods of class balancing can dramatically increase or        decrease the learning rate. In the neural network 900 the        expected learning rate per batch is equal to the intended        learning rate.

Dynamic filtering is performed by removing (1) infrequently used traitvalues/classes as well as (2) classes that we have found to beproblematic—for example avatar sunglasses tend to be used without regardto whether the photograph is shown wearing sunglasses or not.

Because filtering is performed across all traits, removal of the dataitem would result in the removal of a large portion of the training data(since only one trait value being filtered would result in the wholeavatar & photograph to be removed). To prevent this the filtering isdone dynamically, at training time, and independently for each trait.This means that if a single trait is filtered, it will not influence thetraining of the neural network for that avatar & photograph butnon-filtered traits will continue to influence training.

The list of classes for each trait to be filtered is computed beforetraining starts:

-   -   1. Infrequent trait values: If a trait value occurs times in the        training data set it will be marked for dynamic filtering if        where is the number of items any value for that trait occurs in        the training data and is the number of values for that trait.        Note that many other filtering conditions could be used.    -   2. Blacklisted traits: There is an explicit black list of trait        values to be marked for dynamic filtering.

In both cases the balancing weights for the trait values marked fordynamic filtering are set to 0. This makes the gradient for that branchof the fanout 0 which prevents that branch of the fanout, correspondingto the trait with the value that was dynamically filtered out, to notcontribute to training for that avatar & photograph.

For dynamic balancing, each trait (e.g., hair tone) can take on one ofmultiple values/classes (e.g., dark brown). Because some hair colors aremuch more popular than others the classifier could learn to predict themost popular class and be right most of the time. To address this aweight multiplier for each class is calculated and applied duringtraining. This causes the neural network training to take larger stepsfor less frequent classes (i.e., less frequent implies there will befewer relevant steps encountered).

The formula for computing the balancing weight multiplier (nw_(c)) is asfollows:

${n\; w_{c}} = \frac{w_{c}}{w \cdot p}$ Normalization ensures that thelearning rate stays constant across all traits. This is based on thefollowing formula for expect learning rate E[α] = Σ_(c)p_(c)w_(c) and tokeep the learning rate constant E[α] = 1.$w_{c} = \frac{1}{\;^{\sqrt{p_{c}}}}$ Non-normalized weight is inverselyproportional to the probability of a given${value}\text{/}{{class}.{\mspace{11mu} \;}{Using}}\mspace{14mu} \frac{1}{p_{c}}\mspace{14mu} {overcompensates}$and less frequent values/classes dominate more frequent ones.$p_{c} = \frac{f_{c}}{\Sigma_{c}f_{c}}$ The probability of a givenvalue/class as computed from class frequencies encountered in thetraining dataset for a given trait.

The neural network 900 decomposes each trait into multiple subtraits.This increases the training data for each trait and reduces the answerspace. For example, jaw style is decomposed into jaw shape style (Ushaped, V shaped, Double chin) and jaw width (narrow, medium, wide). Atrait value corresponds to one class to be classified. For example, thetrait “jaw” may have values “u-narrow”, “u-wide”, “v-narrow” and“v-wide” and each one of these is a class that can be predicted.

One trait (e.g., jaw) can be decomposed into multiple subtraits and theneural network 900 can train on subtraits instead of or in addition totraits. For example, jaw style can be decomposed to jaw shape style (Ushaped, V shaped, Double chin) and jaw width (narrow, medium, wide).This increases the training data for each trait and reduces the answerspace.

For example, the answer space for jaw would have 9 classes: u-narrow,u-medium, u-wide, v-narrow, v-medium, v-wide, double-narrow,double-medium, double-wide. However, decomposing yields 3+3 classesacross two subtraits. Jaw-style: u,v,double and jaw-width: narrow,medium, wide. Even though multiplying these two yeilds 9 the neuralnetwork 900 can sometimes do a better job on the subtrait prediction.

Distribution of traits is shown below. The data is listed as “traitname” “number of values for the least represented class”, “number ofvalues for the most represented class”. This was generated on a sampleof the data so the counts are not representative of the size of thetraining data but are relatively correct to compute the relativefrequency of a given value/class.

jaw/name 1578 21043

jaw/chin-type 5781 33483

hair/type 6240 28912

glasses/id 2 20660

eye/asian-eye 6103 49706

eye_size/id 11172 20064

face_proportion/id 1894 32508

eye/lid-type 7019 38204

eye/skew 6250 38324

brow/name 323 27350

brow_tone/id 6 12780

skin_tone/id 6 20352

hair/length 758 24438

nose/size 7452 29801

nose/name 83 29463

hair_tone/id 10 9863

hair/id 13 3475

pupil_tone/id 174 22263

mouth/lips 10379 24285

blush_tone/id 4 5819

beard/id 17 26496

eye_spacing/id 3746 39344

beard_tone/id 4 16691

brow/thickness 5349 27837

hair/special 146 146

eye/height 26793 29016

The neural network 900 uses transfer learning to handle new artreleases, e.g., keeping the neural network 900 up-to-date: atop layer isreconstructed to handle new classes and the bottom layer weights arekept the same. In this setup, the learning rate for the top layer is setto our usual learning rate while the bottom layer learning rate can beup about 100 times smaller. Afterwards, the new neural network 900 istrained only on the new dataset. This enables: shorter training timebecause we are only training on the new dataset which is significantlysmaller; and conflicts between the new and the old data are avoided. Anexample to this conflict: after a new “Eye” release, users with similarlooking eyes may now begin picking the new eye option, while in the olddataset that option does not exist.

Squashing the values between 0 and 1 was no worse than mean and stdnormalization (most likely because we use batch norm).

In the neural network 900, facial detection is done before training. Aninput image is shown from the left and goes through convolutions,subsampling, convolutions, more subsampling, full connections, Gaussianconnections and then output. A convolution neural network (e.g.,mobilenet) can be used prior to the full connection layers. There is onefully connected layer for each trait or dimension (see FIG. 11).

The input would be an image of the cropped faced. The neural network 900then learns the subsequent layer which is an image feature extractorlayer (eg. “vertical line” or “blue”). The neural network 900 will thenalso learn all the intermediate layers that become more and moreabstract the further you go (eg. “large eyes”, “small eyes”) until itfinal predicts the output (eg. “bitmoji eye style 1” or “bitmoji eyestyle 2”).

Each layer (e.g., C1) is transformed into the following layer (eg. S2)by essentially a matrix multiplication and a nonlinear function. Eachmatrix entry is called a weight as are any parameters of the nonlinearfunction (e.g., the slope). Weights are floating point numbers like 0.1or −5.1. Bottom weights would correspond to the weights of the bottomlayers of the network. The weights are randomly initialized andautomatically learned by the training process.

FIG. 10 illustrates a method of pretraining 1000 in accordance with oneembodiment. First, an encrypted photograph and user-designed avatar isreceived (1002). The photograph is decrypted (1004) and a face, if anyin the photograph, is detected (1006). If (1008) there is no face, thenthe data is discarded (1028). Otherwise, if (1010) a phone that took thephotograph is not supported, the data is also discarded (1028). Data isalso discarded (1028) if the avatar style is not supported (1012).Finally, data is also discarded (1028) if the user-design avatar is notsufficiently different (1014) from a default avatar. If the data is notfiltered (1008-1014), then bad traits are ignored (1016), e.g., forsunglasses in avatar when sunglasses are not present in the photograph.Then traits are broken up into substraits (1018) and labels affixed(1020). The photograph is cropped (1024) so that only the face is shown,a histogram of values for each trait is updated (1022) and then added(1026) to training data for the neural network 900.

FIG. 11 illustrates a method of neural network training 1100 inaccordance with one embodiment. First, image pixels 1101 are receivedand normalized (1104). Then a convolutional neural network operates onthe images to the extractor layer (1106). The fully connected layersthen train on the subtraits, such as jaw width (1108), jaw shape, hairstyle, etc. The fully connected layers will proceed through logits(1108), balance (1100), prediction (1112) and ignore (1114) bad data.

Neural network 900 training is done via gradient descent optimization.At a given step the network weights are set to some value. The gradientis computed based on the difference between what the network predictedand the actual avatar the user created. A step is then taken in thedirection to minimize this difference. The size of this step ismodulated by the learning rate parameter as shown in curve 1300 in FIG.13.

In the context of transfer learning the bottom layers are trained with asmaller learning rate meaning that the network weights for the bottomlayers are changed more slowly (smaller steps) than the top layers. Thetop layers are being training from scratch as the structure of thenetwork top layers changes when new trait values are introduced.

The neural network 900 uses the following parameters for training:

default_adam_params = { “learning_rate”: 0.001, “beta1”: 0.9, “beta2”:0.999, “epsilon”: 1e-08, }

FIG. 12 illustrates a method of generating an avatar 1200 in accordancewith one embodiment. After the neural network 900 is trained, thetrained neural network 900 can reside on a user device (e.g., clientdevice 102) and/or server (e.g., messaging server system 108 orelsewhere). The Imager 810 receives (1206) a facial image (e.g.,self-photograph, selfie, etc.) and then the neural network 900 generates(1208) and avatar. The client device 102 can then transmit a message(e.g., ephemeral message 502) with the generated avatar associated withit.

Accordingly, embodiments provide a way to automatically generate anavatar and therefore enable graphically identifying message senders.Example embodiments include:

1. A machine-implemented example method of transmitting a message,comprising:

receiving an image;

generating an avatar with a trained neural network based on the image,the trained neural network predicting multiple trait values for theavatar; and

sending a message with the generated avatar.

2. The example method of claim 1, wherein the generating furthercomprises generating the avatar based on subtraits.

3. The example method of claim 1, further comprising balancing of adataset to train the neural network independently across multipletraits.

4. The example method of claim 3, wherein the balancing comprisesapplying a weight multiplier for each trait value, the weight multiplieris based on an inverse square root of a probability of a given traitvalue.

5. The example method of claim 1, further comprising filtering a datasetto train the neural network across multiple traits.

6. The example method of claim 5, wherein the filtering includesremoving avatars from the dataset that include a default value for atrait.

7. The example method of claim 6, wherein default values for a traitinclude skin tone, hair style, and pupil tone.

8. The example method of claim 5, wherein the filtering is performedindependently for each trait.

9. The example method of claim 5, wherein the filtering filtersblacklisted traits.

10. An example machine-readable storage device embodying instructionsthat, when executed by a machine, cause the machine to performoperations comprising: receiving an image;

generating an avatar with a trained neural network based on the image,the trained neural network predicting multiple trait values for theavatar; and

sending a message with the generated avatar.

11. An example system, comprising:

one or more processors of a machine; and

a memory storing instructions that, when executed by the one or moreprocessors, cause the machine to perform operations comprising:

receiving an image;

generating an avatar with a trained neural network based on the image,the trained neural network predicting multiple trait values for theavatar; and

sending a message with the generated avatar.

12. The example system of claim 11, wherein the generating furthercomprises generating the avatar based on subtraits.

13. The example system of claim 11, wherein the operations furthercomprise balancing of a dataset to train the neural networkindependently across multiple traits.

14. The example system of claim 13, wherein the balancing comprisesapplying a weight multiplier for each trait value, the weight multiplieris based on an inverse square root of a probability of a given traitvalue.

15. The example system of claim 11, wherein the operations furthercomprise filtering a dataset to train the neural network across multipletraits.

16. The example system of claim 15, wherein the filtering includesremoving avatars from the dataset that include a default value for atrait.

17. The example system of claim 16, wherein default values for a traitinclude skin tone, hair style, and pupil tone.

18. The example system of claim 15, wherein the filtering is performedindependently for each trait.

19. The example system of claim 15, wherein the filtering filtersblacklisted traits.

20. The example system of claim 19, wherein the message is an EphemeralMessage.

“Signal Medium” refers to any intangible medium that is capable ofstoring, encoding, or carrying the instructions for execution by amachine and includes digital or analog communications signals or otherintangible media to facilitate communication of software or data. Theterm “signal medium” shall be taken to include any form of a modulateddata signal, carrier wave, and so forth. The term “modulated datasignal” means a signal that has one or more of its characteristics setor changed in such a matter as to encode information in the signal. Theterms “transmission medium” and “signal medium” mean the same thing andmay be used interchangeably in this disclosure.

“Communication Network” refers to one or more portions of a network thatmay be an ad hoc network, an intranet, an extranet, a virtual privatenetwork (VPN), a local area network (LAN), a wireless LAN (WLAN), a widearea network (WAN), a wireless WAN (WWAN), a metropolitan area network(MAN), the Internet, a portion of the Internet, a portion of the PublicSwitched Telephone Network (PSTN), a plain old telephone service (POTS)network, a cellular telephone network, a wireless network, a Wi-Fi®network, another type of network, or a combination of two or more suchnetworks. For example, a network or a portion of a network may include awireless or cellular network and the coupling may be a Code DivisionMultiple Access (CDMA) connection, a Global System for Mobilecommunications (GSM) connection, or other types of cellular or wirelesscoupling. In this example, the coupling may implement any of a varietyof types of data transfer technology, such as Single Carrier RadioTransmission Technology (1×RTT), Evolution-Data Optimized (EVDO)technology, General Packet Radio Service (GPRS) technology, EnhancedData rates for GSM Evolution (EDGE) technology, third GenerationPartnership Project (3GPP) including 3G, fourth generation wireless (4G)networks, Universal Mobile Telecommunications System (UMTS), High SpeedPacket Access (HSPA), Worldwide Interoperability for Microwave Access(WiMAX), Long Term Evolution (LTE) standard, others defined by variousstandard-setting organizations, other long-range protocols, or otherdata transfer technology.

“Processor” refers to any circuit or virtual circuit (a physical circuitemulated by logic executing on an actual processor) that manipulatesdata values according to control signals (e.g., “commands”, “op codes”,“machine code”, etc.) and which produces corresponding output signalsthat are applied to operate a machine. A processor may, for example, bea Central Processing Unit (CPU), a Reduced Instruction Set Computing(RISC) processor, a Complex Instruction Set Computing (CISC) processor,a Graphics Processing Unit (GPU), a Digital Signal Processor (DSP), anApplication Specific Integrated Circuit (ASIC), a Radio-FrequencyIntegrated Circuit (RFIC) or any combination thereof. A processor mayfurther be a multi-core processor having two or more independentprocessors (sometimes referred to as “cores”) that may executeinstructions contemporaneously.

“Machine-Storage Medium” refers to a single or multiple storage devicesand/or media (e.g., a centralized or distributed database, and/orassociated caches and servers) that store executable instructions,routines and/or data. The term shall accordingly be taken to include,but not be limited to, solid-state memories, and optical and magneticmedia, including memory internal or external to processors. Specificexamples of machine-storage media, computer-storage media and/ordevice-storage media include non-volatile memory, including by way ofexample semiconductor memory devices, e.g., erasable programmableread-only memory (EPROM), electrically erasable programmable read-onlymemory (EEPROM), FPGA, and flash memory devices; magnetic disks such asinternal hard disks and removable disks; magneto-optical disks; andCD-ROM and DVD-ROM disks The terms “machine-storage medium,”“device-storage medium,” “computer-storage medium” mean the same thingand may be used interchangeably in this disclosure. The terms“machine-storage media,” “computer-storage media,” and “device-storagemedia” specifically exclude carrier waves, modulated data signals, andother such media, at least some of which are covered under the term“signal medium.”

“Component” refers to a device, physical entity, or logic havingboundaries defined by function or subroutine calls, branch points, APIs,or other technologies that provide for the partitioning ormodularization of particular processing or control functions. Componentsmay be combined via their interfaces with other components to carry outa machine process. A component may be a packaged functional hardwareunit designed for use with other components and a part of a program thatusually performs a particular function of related functions. Componentsmay constitute either software components (e.g., code embodied on amachine-readable medium) or hardware components. A “hardware component”is a tangible unit capable of performing certain operations and may beconfigured or arranged in a certain physical manner. In various exampleembodiments, one or more computer systems (e.g., a standalone computersystem, a client computer system, or a server computer system) or one ormore hardware components of a computer system (e.g., a processor or agroup of processors) may be configured by software (e.g., an applicationor application portion) as a hardware component that operates to performcertain operations as described herein. A hardware component may also beimplemented mechanically, electronically, or any suitable combinationthereof. For example, a hardware component may include dedicatedcircuitry or logic that is permanently configured to perform certainoperations. A hardware component may be a special-purpose processor,such as a field-programmable gate array (FPGA) or an applicationspecific integrated circuit (ASIC). A hardware component may alsoinclude programmable logic or circuitry that is temporarily configuredby software to perform certain operations. For example, a hardwarecomponent may include software executed by a general-purpose processoror other programmable processor. Once configured by such software,hardware components become specific machines (or specific components ofa machine) uniquely tailored to perform the configured functions and areno longer general-purpose processors. It will be appreciated that thedecision to implement a hardware component mechanically, in dedicatedand permanently configured circuitry, or in temporarily configuredcircuitry (e.g., configured by software), may be driven by cost and timeconsiderations. Accordingly, the phrase “hardware component” (or“hardware-implemented component”) should be understood to encompass atangible entity, be that an entity that is physically constructed,permanently configured (e.g., hardwired), or temporarily configured(e.g., programmed) to operate in a certain manner or to perform certainoperations described herein. Considering embodiments in which hardwarecomponents are temporarily configured (e.g., programmed), each of thehardware components need not be configured or instantiated at any oneinstance in time. For example, where a hardware component comprises ageneral-purpose processor configured by software to become aspecial-purpose processor, the general-purpose processor may beconfigured as respectively different special-purpose processors (e.g.,comprising different hardware components) at different times. Softwareaccordingly configures a particular processor or processors, forexample, to constitute a particular hardware component at one instanceof time and to constitute a different hardware component at a differentinstance of time. Hardware components can provide information to, andreceive information from, other hardware components. Accordingly, thedescribed hardware components may be regarded as being communicativelycoupled. Where multiple hardware components exist contemporaneously,communications may be achieved through signal transmission (e.g., overappropriate circuits and buses) between or among two or more of thehardware components. In embodiments in which multiple hardwarecomponents are configured or instantiated at different times,communications between such hardware components may be achieved, forexample, through the storage and retrieval of information in memorystructures to which the multiple hardware components have access. Forexample, one hardware component may perform an operation and store theoutput of that operation in a memory device to which it iscommunicatively coupled. A further hardware component may then, at alater time, access the memory device to retrieve and process the storedoutput. Hardware components may also initiate communications with inputor output devices, and can operate on a resource (e.g., a collection ofinformation). The various operations of example methods described hereinmay be performed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implementedcomponents that operate to perform one or more operations or functionsdescribed herein. As used herein, “processor-implemented component”refers to a hardware component implemented using one or more processors.Similarly, the methods described herein may be at least partiallyprocessor-implemented, with a particular processor or processors beingan example of hardware. For example, at least some of the operations ofa method may be performed by one or more processors 1004 orprocessor-implemented components. Moreover, the one or more processorsmay also operate to support performance of the relevant operations in a“cloud computing” environment or as a “software as a service” (SaaS).For example, at least some of the operations may be performed by a groupof computers (as examples of machines including processors), with theseoperations being accessible via a network (e.g., the Internet) and viaone or more appropriate interfaces (e.g., an API). The performance ofcertain of the operations may be distributed among the processors, notonly residing within a single machine, but deployed across a number ofmachines. In some example embodiments, the processors orprocessor-implemented components may be located in a single geographiclocation (e.g., within a home environment, an office environment, or aserver farm). In other example embodiments, the processors orprocessor-implemented components may be distributed across a number ofgeographic locations.

“Carrier Signal” refers to any intangible medium that is capable ofstoring, encoding, or carrying instructions for execution by themachine, and includes digital or analog communications signals or otherintangible media to facilitate communication of such instructions.Instructions may be transmitted or received over a network using atransmission medium via a network interface device.

“Computer-Readable Medium” refers to both machine-storage media andtransmission media. Thus, the terms include both storage devices/mediaand carrier waves/modulated data signals. The terms “machine-readablemedium,” “computer-readable medium” and “device-readable medium” meanthe same thing and may be used interchangeably in this disclosure.

“Client Device” refers to any machine that interfaces to acommunications network to obtain resources from one or more serversystems or other client devices. A client device may be, but is notlimited to, a mobile phone, desktop computer, laptop, portable digitalassistants (PDAs), smartphones, tablets, ultrabooks, netbooks, laptops,multi-processor systems, microprocessor-based or programmable consumerelectronics, game consoles, set-top boxes, or any other communicationdevice that a user may use to access a network.

“Ephemeral Message” refers to a message that is accessible for atime-limited duration. An ephemeral message may be a text, an image, avideo and the like. The access time for the ephemeral message may be setby the message sender. Alternatively, the access time may be a defaultsetting or a setting specified by the recipient. Regardless of thesetting technique, the message is transitory.

1. A machine-implemented method of transmitting a message, comprising:training a neural network with a dataset of facial images andcorresponding avatars, the neural network having a fanout of a topclassification layer; receiving a first facial image; generating a firstavatar with the trained neural network based on the first facial image,the trained neural network predicting multiple trait values for thefirst avatar; and sending a message with the generated avatar, thegenerated avatar identifying a sender of the message.
 2. The method ofclaim 1, wherein the generating further comprises generating the firstavatar based on subtraits.
 3. The method of claim 1, wherein thetraining further comprises balancing of the dataset to train the neuralnetwork independently across multiple traits.
 4. The method of claim 3,wherein the balancing comprises applying a weight multiplier for eachtrait value, the weight multiplier is based on an inverse square root ofa probability of a given trait value.
 5. The method of claim 1, whereinthe training further comprises filtering the dataset to train the neuralnetwork across multiple traits.
 6. The method of claim 5, wherein thefiltering includes removing avatars from the dataset that include adefault value for a trait.
 7. The method of claim 6, wherein defaultvalues for a trait include skin tone, hair style, and pupil tone.
 8. Themethod of claim 5, wherein the filtering is performed independently foreach trait.
 9. The method of claim 5, wherein the filtering filtersblacklisted traits.
 10. A machine-readable storage device embodyinginstructions that, when executed by a machine, cause the machine toperform operations comprising: training a neural network with a datasetof facial images and corresponding avatars, the neural network having afanout of a top classification layer; receiving a first facial image;generating a first avatar with the trained neural network based on thefirst image, the trained neural network predicting multiple trait valuesfor the first avatar; and sending a message with the generated avatar,the generated avatar identifying a sender of the message.
 11. A system,comprising: one or more processors of a machine; and a memory storinginstructions that, when executed by the one or more processors, causethe machine to perform operations comprising: training a neural networkwith a dataset of facial images and corresponding avatars, the neuralnetwork having a fanout of a top classification layer; receiving firstfacial image; generating a first avatar with the trained neural networkbased on the first image, the trained neural network predicting multipletrait values for the first avatar; and sending a message with thegenerated avatar, the generated avatar identifying a sender of themessage.
 12. The system of claim 11, wherein the generating furthercomprises generating the avatar based on subtraits.
 13. The system ofclaim 11, wherein the training operation further comprises balancing ofa dataset to train the neural network independently across multipletraits.
 14. The system of claim 13, wherein the balancing comprisesapplying a weight multiplier for each trait value, the weight multiplieris based on an inverse square root of a probability of a given traitvalue.
 15. The system of claim 11, wherein the training operationfurther comprises filtering a dataset to train the neural network acrossmultiple traits.
 16. The system of claim 15, wherein the filteringincludes removing avatars from the dataset that include a default valuefor a trait.
 17. The system of claim 16, wherein default values for atrait include skin tone, hair style, and pupil tone.
 18. The system ofclaim 15, wherein the filtering is performed independently for eachtrait.
 19. The system of claim 15, wherein the filtering filtersblacklisted traits.
 20. The system of claim 19, wherein the message isan Ephemeral Message.